ISSN 2229-5518 359 Automatic Generation Control in Three Area Interconnected Power System of Thermal Generating Unit using Evolutionary Controller Ashish Dhamanda 1, A.K.Bhardwaj 2 12 Department of Electrical Engineering, SSET, SHIATS, Allahabad, U.P, India Abstract This paper deals to obtain the dynamic response of load frequency and corresponding tie-line power of an automatic generation control (AGC) in three area interconnected thermal power system by using three different controller; One is Conventional (PI) Controller, Second is Intelligent (Fuzzy) Controller and Third is Evolutionary (GA for tuning of PID Controller) Controller. In this paper Evolutionary controller are proposed for improving the performance of load frequency and tie-line power and their dynamic responses are compared with the conventional and intelligent controller s responses. The results indicate that the proposed controller exhibit better performance and satisfy the automatic generation control requirements with a reasonable dynamic response. The performances of the controllers are simulated using MATLAB/SIMULINK software. Keywords Proportional Plus Integral Plus Derivative (PID), Fuzzy Controller, Intelligent Controller, Genetic Algorithm (GA). 1 INTRODUCTION past decades [13]. Most of the earlier works in the area of AGC pertain to thermal systems with non-reheat and reheat type UTOMATIC Generation Control (AGC) is a very important issue in power system operation and control turbines for single and two area with different controller but A for supplying sufficient and reliable electric power with good relatively lesser attention has been devoted to the comparison quality. AGC with load following is treated as an ancillary of PI, fuzzy and GA controllers. Three area thermal power service that is essential for maintaining the electrical system system incorporating reheat type turbine and linearized models reliability at an adequate level [12] recent years, major changes of governors, non-reheat turbines and reheat turbines are taken have been introduced into the structure of electric power for simulation of the system. utilities all around the world. The successful operation in power system requires the matching of total generation with total 2 AGCIN THERMAL GENERATION SYSTEM load demand and associated system losses. As the demand deviates from its normal value with an unpredictable small amount, the operating point of power system changes, and hence, system may experience deviations in nominal system frequency which may yield undesirable effects. So the The role of AGC in interconnected power system is to maintain the system frequency and tie-line power at nominal value after some kind of perturbation arises in the system. To maintain the electrical power system in normal operating state, the generated power should match with power demand plus objective of AGC in interconnected thermal generating unit is associated losses. However, in practical power system, the load to maintain the system frequency and tie line power at nominal is continuously changing with respect to time. Therefore, the value (60 Hz) [4], [5]. A control strategy is needed to maintain power balance equilibrium cannot be satisfied in abnormal constancy of frequency and tie-line power and also achieves state. In primary control action also called without controller, zero steady state error. The PI and fuzzy controller employed when the power system is said to be at stable state, primary control action takes place within an area to suppress to solve AGC problem and these controller gives the good frequency oscillations. On the other hand, when the load response, reduces the oscillation & steady state error but the fluctuations are more, primary control action are not adequate GA after tuning of PID controller gives the better result to control. over the conventional and intelligent controller. A literature To overcome the problem of primary control action, the survey shows that the load frequency control (LFC) of power secondary control action also called with controller, need to systems has been investigated by many researchers over the apply, these controllers are set for a particular operating condition and they take care of small changes condition and Ashish Dhamanda, is with Electrical Engineering Department, Shepherd School they take care of small changes in load demand without of Engineering and Technology, Sam Higginbottom Institute of Agriculture Technology and Sciences, Deemed University Allahabad, U.P India. exceeding the prescribed limits of frequency. These control (e-mail: dhamanda_ashish@yahoo.co.in) action comprises of different controller like conventional, A.K.Bhardwaj, is with Electrical Engineering Department, SSET, Sam intelligent and evolutionary controller [2], [3], [8], [9], [11], Higginbottom Institute of Agriculture Technology and Sciences, Deemed [16]. Three area AGC model of thermal generating system is University Allahabad, 211007, India. (e.mail: dr.akbhardwaj65@rediff.com) shown below in fig. 1. 2015
ISSN 2229-5518 360 Fig.1 Three Area AGC Model of Thermal Generating System 3.1 Conventional (PI) Controller Let us consider the problem of controlling the power output of the generators of a closely knit electric area so as to maintain the scheduled frequency. All the generators in such an area constitute a coherent group so that all the generators speed up and slow down together maintaining their relative power angles. Such an area is defined as a control area. To understand the AGC problem of frequency control, let us consider a single turbo-generator system supplying an isolated load [2]. To simplicity the frequency-domain analyses, transfer functions are used to model each component of the area [4]. Transfer function of governor is Transfer function of turbine is Transfer function of Reheat turbine is Transfer function of generator is. Dynamic response of automatic frequency control loop is F(s) () This equation can be written as, [3] F(s) P 3 CONTROL METHODOLOGY (1) (2) (3) (4) (5) (6) Controller determines the value of controlled variable, compare the actual value to the desired value (reference input), determines the deviation and produces a control signal that will reduce the deviation to zero or to a smallest possible value. In automatic 2015 generation control of thermal generating unit need to control or maintain the frequency constancy, reduced oscillation and zero steady state error, so following types of controller are used, [10] PI controller is also known as proportional plus integral controller. This controller are using from many year back for controlling such action with maintaining their performance. This is a combination of proportional and integral control action shown in fig.1 Fig. 2 Proportional Plus Integral Control Scheme Model Control Area Input = K p Error Signal + K p K i Error Signal (7) 3.2 Intelligent (Fuzzy Logic) Controller Fuzzy logic establishes the rules of a nonlinear mapping. There has been extensive use of fuzzy logic in control applications. One of its main advantages is that controller parameters can be changed very quickly depending on the system dynamics because no parameter estimation is required in designing controller for nonlinear systems. Fuzzy logic controller is shown below [6] in fig.3,
ISSN 2229-5518 361 The current population reproduces new individuals that are called the new generation. The new individuals of the new generation are supposed to have better performance than the individuals of the previous generation. GA have been successfully implemented in the area of industrial electronics, system identification, control robotics, pattern recognition, planning and scheduling [14],[15], shown in fig.5. Fig. 3 Fuzzy Logic Control Scheme Model The inputs of the proposed fuzzy controller are e, and rate of change in ce. The appropriate membership function and fuzzy rule base is shown in below in fig.4 and table 1, where 7 membership function, NB, NM, NS, Z, PS, PM, and PB represent negative big, negative medium, negative small, zero, positive small, positive medium, and positive big, respectively make 49 (7 7) rule [7]. Fig. 5 Flow chart for tuning of PID using genetic algorithm (GA) 4 SIMULATION RESULTS Fig. 4 Fuzzy Inference System Editor TABLE 1 Fuzzy Inference Rule All the results are carried out by using MATLAB/Simulink to investigate the performance of three areas thermal system. The power system parameters are given in appendix. The step load disturbance of 0.01 p.u. was applied in three areas for all the cases and deviations in frequency and corresponding tie-line power were investigated. The AGC performance through PI and Fuzzy logic controller is compared with GA (Using tuning of PID controller) controller. The change in frequency and corresponding tie-line deviation under the load disturbances of 0.01 p.u. in three areas are shown in fig 6 to fig 23. Comparative value of settling time shown in table 2, it is observed that the evolutionary (GA for tuning of PID Controller) controller improve the dynamic performance of the system as compared to the conventional (PI) and intelligent (Fuzzy Logic) controllers. 3.3 Evolutionary (GA Controller) Controller The genetic algorithm is a robust optimization controller based on natural selection. A possible solution to a specific problem is seen as an individual. A collection of a number of individuals is called a population. Fig. 6 Frequency Response of Area1 with PI Controller 2015
ISSN 2229-5518 362 Fig. 7 Frequency Response of Area2 with PI Controller Fig. 11 Frequency Response of Area3 with Fuzzy Controller Fig. 8 Frequency Response of Area3 with PI Controller Fig. 12 Frequency Response of Area1 with GA Controller Fig. 9 Frequency Response of Area1 with Fuzzy Controller Fig. 13 Frequency Response of Area2 with GA Controller Fig. 10 Frequency Response of Area2 with Fuzzy Controller Fig. 14 Frequency Response of Area3 with GA Controller 2015
ISSN 2229-5518 363 Fig. 15 Tie-Line Power Response of Area1 with PI Controller Fig. 19 Tie-Line Power Response of Area2 with Fuzzy Controller Fig. 16 Tie-Line Power Response of Area2 with PI Controller Fig. 20 Tie-Line Power Response of Area3 with Fuzzy Controller Fig. 17 Tie-Line Power Response of Area3 with PI Controller Fig. 21 Tie-Line Power Response of Area1 with GA Controller Fig. 18 Tie-Line Power Response of Area1 with Fuzzy Controller Fig. 22 Tie-Line Power Response of Area2 with GA Controller 2015
ISSN 2229-5518 364 Fig. 23 Tie-Line Power Response of Area3 with GA Controller Table 2 Comparative value of settling time f Nominal system frequency f Change in supply frequency D i System damping area i T sg Speed governor time constant T t Steam turbine time constant T ps Power system time constant K sg Speed governor gain constant K t Steam turbine gain constant K ps Power system gain constant b i Frequency bias parameter P Di Incremental load change in area i i Subscript referring to area 1 2 3 etc. H Inertia constant R Speed regulation of governor a Ratio of rated power of a pair of areas four area system T Synchronous coefficient of tie-line system P tie max Tie-line power Controllers PI 8 REFERENCES GA (Using 17 17 18 24 15 28 Tuning of PID Controller) 5 CONCLUSIONS This paper investigates the performance of automatic generation control of three area thermal power system. To demonstrate the effectiveness of proposed controller, evolutionary (Genetic Algorithm for tuning of PID controller) controller, the control strategy based on intelligent (Fuzzy Logic) and conventional (PI) controller is applied. The performance of these controllers is evaluated through the simulation. The results are tabulated in Table II respectively. The results of proposed controller have been compared with conventional and intelligent controller and it shows that the proposed controller give good dynamic performances and results. So it can be concluded that the evolutionary controller give better settling performance than the intelligent and conventional controllers. 6 APPENDIX Frequency Deviation Power System Parameters are as follows: f=60hz; R 1 =R 2 =R 3 =2.4Hz/p.uMW; T sg1 =T sg2 = T sg3 = 0.08Sec; T ps1 =T ps2 =T ps3 =20Sec; Tt 1 = Tt 2 = Tt 3 =0.3 Sec; Tr 1 =Tr 2 = Tr 3 =10Sec; Kr 1 =0.5TU; Kr 2 =3.33TU; Kr 3 =3TU; H 1 =H 2 =H 3 =5MW-S/MVA; P ri =2000MW, K ps123 =120 Hz.p.u/MW; K sg123 =K t123 =1; D i =8.33*10-3 p.umw/hz.; b i =0.425p.u.MW/Hz; P Di =0.01 p.u; T 12 = T 23 =T 31 =0.0867MW/Radian; P tie max =200MW; a 12 =a 23 =a 34 =a 41 =1; P r1 = P r2 =P r3 =2000MW; Nomenclature AGC Automatic Generation Control P ri Rated power capacity of area i Settling Time (Sec) Tie Line Power Deviation Area1 Area2 Area3 Area1 Area2 Area3 28 30 30 66 75 52 Fuzzy 21 27 26 35 56 35 2015 7 ACKNOWLEDGEMENT This work is supported by electrical engineering department, Sam Higginbottom Institute of Agriculture Technology and Sciences. Allahabad, India. [1] A. Magla, J. Nanda, Automatic Generation Control of an Interconnected Hydro- Thermal System Using Conventional Integral and Fuzzy logic Control, in Proc. IEEE Electric Utility Deregulation, Restructuring and Power Technologies, Apr 2004. [2] D. P. Kothari, Nagrath Modern Power System Analysis ; Tata McGraw Hill, Third Edition, 2003. [3] Elgerd O. I, Elctric Energy System Theory; An Introduction McGraw Hill, 1971. [4] Surya Prakash, S K Sinha, Four Area Load Frequency Control of Interconnected Hydro-Thermal Power System by Intelligent PID Control Controller ; 978-1-4673-0455-9/12, IEEE 2012. [5] K.P.Singh Parmar, S.Majhi, D.P.Kothari, Optimal Load Frequency Control of an Interconnected Power System ; MIT International Journal of Electrical and Instrumentation Engineering Vol. 1, No. 1, Jan.2011, pp. 1-5, ISSS No. 2230-7656, MIT Publications. [6] Surya Prakash, S K Sinha, Intelligent PI Control Controller in Four Area Load Frequency Control of Interconnected Hydro-thermal Power System ; 978-1-4673-0210-4/12, IEEE 2012. [7] Rishabh Verma, Shalini Pal, Sathans, Intelligent Automatic Generation Control of Two-Area Hydrothermal Power System using ANN and Fuzzy Logic ;978-0-7695-4958-3/13, IEEE 2013. [8] Kiran Kumar Challa, P.S.Nagendra Rao, Analysis and Design of Controller for Two Area Thermal-Hydro-Gas AGC System ; 978-1-4244-7781-4/10, IEEE 2010. [9] S. Sivanagaraju, G. Sreenivasan, Power System Operation and Control. PEARSON (2011). [10] S.Hasan Saeed, Automatic Control System, 2006. [11] Hadi Sadat, Power System Analysis. Tata MCGraw Hill 1999. [12] Ranjit Roy, S. P. Ghoshal, Praghnesh Bhatt, Evolutionary Computation based Four-Area Automatic Generaton Control in Restructured Environment ; 978-1-4244-4331-4/09, IEEE 2009. [13] Xiangjie Liu, Xiaolei Zhan, Dianwei Quian, Load Frequency Control considering Generation Rate Constraints ; 978-1-4244-6712-9/10, IEEE 2010. [14] Sapna Bhati, Dhiiraj Nitnawwre, Genetic Optimization Tuning of an Automatic Voltage Regulator System ; IJSET, Volume No.1, Issue No. 3, pg: 120-124, ISSN: 2277-1581, 01July 2012. [15] K. F.Man, K. S.Tang and S. Kwong, Genetic algorithm: Concepts and applications ; IEEE Trans. Ind. Electron, vol. 43, no. 5, pp. 519-534, may 1996. [16] Naresh Kumari, A. N. Jha, Automatic Generation Control Using LQR based PI Conreoller for Multi Area interconnected Power System ; Advance in Electronic and Electric Engineering, ISSN 2231-1297, Volume 4, pp. 149-154, November 2, 2014.